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Variational Inference

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TitleStatusHype
Gray-box inference for structured Gaussian process models—0
Group Factor Analysis—0
Half-AVAE: Adversarial-Enhanced Factorized and Structured Encoder-Free VAE for Underdetermined Independent Component Analysis—0
Half-VAE: An Encoder-Free VAE to Bypass Explicit Inverse Mapping—0
Hamiltonian Policy Optimization—0
Hamiltonian Policy Optimization in Reinforcement Learning—0
Refining Latent Representations: A Generative SSL Approach for Heterogeneous Graph Learning—0
Hierarchical Bayes Autoencoders—0
Hierarchical Bayesian Personalized Recommendation: A Case Study and Beyond—0
Hierarchical Implicit Models and Likelihood-Free Variational Inference—0
Hierarchical mixtures of Unigram models for short text clustering: The role of Beta-Liouville priors—0
Hierarchical models for neural population dynamics in the presence of non-stationarity—0
Hierarchical Sparse Bayesian Multitask Model with Scalable Inference for Microbiome Analysis—0
HierVST: Hierarchical Adaptive Zero-shot Voice Style Transfer—0
Higher-Order Inference for Multi-Class Log-Supermodular Models—0
High Mutual Information in Representation Learning with Symmetric Variational Inference—0
Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints—0
How particular is the physics of the free energy principle?—0
How to deal with missing data in supervised deep learning?—0
Hybrid Bayesian Neural Networks with Functional Probabilistic Layers—0
Hybrid Variational Autoencoder for Time Series Forecasting—0
Hyperspectral Unmixing Under Endmember Variability: A Variational Inference Framework—0
Variational Hyper-Encoding Networks—0
Identifiable Feature Learning for Spatial Data with Nonlinear ICA—0
Identifying signal and noise structure in neural population activity with Gaussian process factor models—0
Idiographic Personality Gaussian Process for Psychological Assessment—0
iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data—0
Out-of-distribution detection for regression tasks: parameter versus predictor entropy—0
Importance Sampled Stochastic Optimization for Variational Inference—0
Importance Weighted Structure Learning for Scene Graph Generation—0
Importance Weighting and Variational Inference—0
Improved Bayesian Logistic Supervised Topic Models with Data Augmentation—0
Improved Gradient-Based Optimization Over Discrete Distributions—0
Improved Gradient Estimators for Stochastic Discrete Variables—0
Improving Actor-Critic Reinforcement Learning via Hamiltonian Monte Carlo Method—0
Structured Dropout Variational Inference for Bayesian Neural Networks—0
Improving Graph Out-of-distribution Generalization on Real-world Data—0
Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes—0
Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations—0
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models—0
'In-Between' Uncertainty in Bayesian Neural Networks—0
Incorporating Group Prior into Variational Inference for Tail-User Behavior Modeling in CTR Prediction—0
Incorporating Word Correlation Knowledge into Topic Modeling—0
Incremental Variational Inference for Latent Dirichlet Allocation—0
Independent projections of diffusions: Gradient flows for variational inference and optimal mean field approximations—0
Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations—0
Hierarchical Indian Buffet Neural Networks for Bayesian Continual Learning—0
Indian Buffet Process Deep Generative Models for Semi-Supervised Classification—0
Inducing Interpretable Representations with Variational Autoencoders—0
Inference for determinantal point processes without spectral knowledge—0
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